1. Field of Invention
The present invention pertains to the field of Positron Emission Tomography (PET). More particularly, this invention relates to a method for improving the quality of clinical data in PET.
2. Description of the Related Art
The statistical precision of PET coincidence data is characterized by its signal-to-noise ratio (SNR), which is defined as the ratio of the mean N of the data to its root mean square (standard) deviation σ. Using the approximation that the deviations of the true, random and scattered coincidences from their respective mean values are uncorrelated and Poisson distributed, and assuming that a noiseless estimate of the mean of the scatter is available, the SNR of the scatter- and randoms-corrected coincidence data can be expressed as:
      SNR    =                  N        σ            =                                                  (                                                T                  2                                                  T                  +                  S                  +                  kR                                            )                                      1              /              2                                ⁢                      t                          1              /              2                                      =                              NECR                          1              /              2                                ⁢                      t                          1              /              2                                            ,where T is the rate of true coincidences detected, S is the scatter, and R is the randoms. k is a constant between 1 and 2 depending on the variance of the randoms estimate, where k=1 for no noise and k=2 for variance equal to the mean. t is the duration of the acquisition. The quantity
      T    2        T    +    S    +    kR  represents the noise equivalent count rate (NECR). These count rates may either be global or refer to spatially localized regions of the data that correspond to specific regions of interest in the object being imaged. See R. H. Huesman, “A new fast algorithm for the evaluation of regions of interest and statistical uncertainty in computed tomography,” Phys. Med. Biol., vol. 29, pp. 543-552 (1984).
The National Electrical Manufactures Association (NEMA) has defined NECR as a standard for quantifying the count rate performance of PET scanners. See, for example, National Electrical Manufactures Association, Rosslyn, Va., NEMA Standards Publication NU 2-2001, Performance Measurements of Positron Emission Tomographs,(2001); M. E. Daube-Witherspoon, et al., “PET performance measurements using the NEMA NU 2-2001 standard,” J. Nucl. Med., vol. 43, pp. 1398-1409 (October 2002); and S. C. Strother, et al., “Measuring PET scanner sensitivity: Relating countrates to image signal-to-noise ratios using noise equivalent counts,” IEEE Trans. Nuc. Sci., vol. 37, pp. 783-788 (April 1990). The standard measurement is performed on a 20 cm diameter, 70 cm long phantom and involves accurately estimating the scatter and randoms contribution to the data over an extended activity range. This measurement is intended to roughly approximate conditions of human whole-body scans.
Although it quantifies data quality, integral NECR is not necessarily a direct measure of image quality or clinical utility. One reason for this is that an NECR based on total count rates does not distinguish diagnostic from physiological regions of activity, the diagnostic regions being of interest. SNR in localized data regions of interest should be studied in order to make clinically relevant quantitative estimates. Yet integral NECR is meaningful as a relative criterion for selecting patient acquisition parameters to the extent that the rates of useful and extraneous events are simultaneously optimized. While there are exceptions, it is physically plausible that global optimization is usually worthwhile.
It is useful to characterize clinical data in terms of NECR in order to optimize parameters such as patient dose, uptake period, and frame duration. However, as a practical matter, estimation of NECR on patient data is difficult. R. J. Smith et al., “Comparisons of clinical performance with standard measures of PET cameras,” 1998 IEEE NSS and MIC Conf. Rec., no. M6-11 (1999), showed a correlation of the prompts and trues rates as a function of singles rates between patient data and standard phantom measurements, but did not evaluate NECR performance. R. D. Badawi et al., “A simulation-based assessment of the revised NEMA NU-2 70-cm long test phantom for PET,” 2001 IEEE NSS and MIC Conf. Rec., no. M6-6 (2002), compared NEMA 70 cm phantom NECR results to Monte Carlo simulations of three anthropomorphic models on two scanners, employing computed information that is not readily available clinically. C. Lartizien et al., “Optimization of injected dose based on noise equivalent count rates for 2- and 3-dimensional whole-body PET,” J. Nucl. Med., vol. 43, pp. 1268-1278 (September 2002), have also evaluated patient data in terms of NECR. However, these studies used measurements on an anthropomorphic phantom to derive models for the prompt and delayed coincidence rates as a function of singles rates, and did not attempt to fit these models directly to the patient data, relying instead on the similarity between the phantom and patients.
Higher SNR implies relatively less noise in the data and is related to improved image quality. Thus it is desirable to maximize SNR for clinical acquisitions. To do this it is necessary to know the dependence of SNR on the patient's injected dose Dinj of radiopharmaceutical. FIG. 1 illustrates SNR as a function of a patient's injected dose Dinj after a 1 hour uptake.
This relation depends on many quantities including the patient's weight and distribution of attenuating tissue, the distribution of the emitter in the body, the region of the body being scanned, the position of the patient in the scanner, and the uptake period. It also depends on various characteristics of the scanner itself, including its shielding, sensitivity, dead time per detected event, and its energy and time discrimination capabilities. In general, SNR(Dinj) is not known for clinical acquisitions. Only the single point illustrated at 12 in FIG. 1, corresponding to the actual acquisition, can typically be determined.
Direct measurement of SNR(Dinj) on a human subject using the most important radiopharmaceutical, 18F-FDG, takes several hours, during which the subject must remain motionless. 18F-FDG has a half-life of 109.77 min, and the SNR(Dinj) measurement does account for migration of activity in the body. Therefore, such measurement is not feasible. Measurements made with a different, shorter-lived, radioisotope are not relevant to FDG due to potential differences in biodistribution.
Attempts have been made to determine SNR(Dinj) for patients by using Monte Carlo simulations and by making measurements on anthropomorphic phantoms. These techniques do not address all characteristics of particular patient scans and hence do not provide SNR(Dinj) for specific clinical imaging situations, but only offer general guidance. Further, they are time consuming to perform and hence not clinically practical. From the prior art, there is not an efficient, accurate means of estimating a patient- and scan-specific SNR(Dinj), or a quantity proportional to it, from a single clinical measurement.
Once SNR(Dinj)is known, it can be used to determine how to adjust injected dose, as well as other scan parameters, to optimize the tradeoffs among data quality, radiation dose to the patient, and scan time.